Method and system for conveying an example in a natural language understanding application

ABSTRACT

A method ( 300 ) and system ( 100 ) is provided to add the creation of examples at a developer level in the generation of Natural Language Understanding (NLU) models, tying the examples into a NLU sentence database ( 130 ), automatically validating ( 310 ) a correct outcome of using the examples, and automatically resolving ( 316 ) problems the user has using the examples. The method ( 300 ) can convey examples of what a caller can say to a Natural Language Understanding (NLU) application. The method includes entering at least one example associated with an existing routing destination, and ensuring an NLU model correctly interprets the example unambiguously for correctly routing a call to the routing destination. The method can include presenting the example sentence in a help message ( 126 ) within an NLU dialogue as an example of what a caller can say for connecting the caller to a desired routing destination. The method can also include presented a failure dialogue for displaying at least one example that failed to be properly interpreted to ensure that ambiguous or incorrect examples are not presented in a help message.

BACKGROUND

1. Field of the Invention

The present invention relates to the field of natural languageunderstanding (NLU) and, more particularly, to a method and system tofacilitate user interaction in an NLU application.

2. Description of the Related Art

Natural Language Understanding (NUT) systems have been increasinglyutilized for interfacing with software applications, customer supportsystems, embedded devices, and voice interactive based machines. MostNLU systems are employed to interact with a user, to receive text orvoice, and to determine what the user desires the machine to accomplish.NLU systems can interpret a text or spoken utterance for performing aprogrammatic action. Typically, a user speaks into the device or machineand the NLU application performs a responsive action. For example, theNLU application can interpret a caller's request and identify the mostappropriate destination to route the caller to by recognizing contentwithin the spoken request.

Callers often have difficulty using an NLU application because thecaller may not know what word should be spoken to the application. Thecaller can become frustrated when the NLU application misinterprets thecaller's request and routes the caller to an incorrect destination, orsimply does not process or respond to the caller's request. Accordingly,the user may not have a satisfactory experience with the NLUapplication. To improve understanding performance, the NLU system canemploy domain specific vocabularies to process a caller request forrouting a caller to a destination. A different NLU system can be usedfor different applications such as airline reservations, car rentals,hotel reservations, and other service based inquiry systems. The NLUsystem can recognize phrases particular to a certain terminology orfield. Accordingly, the NLU system can be trained to interpret certainphrases to improve interpretation performance. The NLU system can betrained for specific phrases and sentences that are more representativeof the requests callers may typically have with the service offering.

Currently, developing a NLU system is a mostly manual process in whichstatistical models are built from a corpus of user utterances thatrepresent what a caller might say in response to the system prompts. Aspart of the development of these types of applications, developers maymake decisions about examples of legitimate utterances that can bepresented to callers in help prompts messages. However, the sentencesselected by developers to present in the help prompts may be ambiguouslyinterpreted or completely misinterpreted by the NLU statistical modelswith a consequence that the NLU application may not associate thesentences with the correct response to a caller's question or statement.Accordingly, the NLU application's ability to correctly process acaller's request depends on the content and relevance of the sentencesthe developer entered into the NLU database during development. A highlevel of substantive content within the example sentences can berequired within an NLU system for the NLU application to correctlyinterpret spoken requests from the caller. In practice, the developergenerally decides what sentences should be entered into the NLU databaseprior to knowing the target NLU application. The developer alsotypically provides many different examples in anticipation of generallyspoken caller requests since the developer does not know what requeststo expect.

It is very effective for applications to offer assistance to callers byproviding examples phrases through help prompts or otherwise, that thecaller can use to interact with the system. Presently, NLU applicationdevelopment environments provide little support for coming up with orknowing what phrases or sentences the NLU application is capable ofprocessing. Currently, the design of caller examples and the validationof examples is a manual process that is poorly understood by developersand often overlooked in practice. Therefore, there is a need fordevelopers to convey to callers of an NLU application examples ofstatements to facilitate a favorable and responsive interaction with theapplication, and a need to make it easy for developers to providehigh-quality examples. By encouraging users to use examples provided bya developer, the developer can significantly improve the NLU system'susability.

SUMMARY OF THE INVENTION

The invention disclosed herein provides a method and system forinteracting with a natural language understanding system (NLU). Themethod can include the steps of entering at least one example sentenceduring development of a natural language understanding (NLU)application, and conveying the example sentence. In one aspect, theexample sentence is interpreted by an NLU model. The method can furtherinclude presenting the example sentence in a help message of the NLUapplication as an example of what to say to interact with the NLUapplication. The method can include presenting a failure dialog to thedeveloper for displaying at least one example that failed to be properlyinterpreted.

Another aspect of the invention can include a system for creating aNatural Language Understanding (NLU) application. The system can includean example planner that prompts a developer for at least one examplesentence, and a validation unit connected to the example planner toincrease a likelihood that an NLU model correctly interprets the examplesentence. The system can include a help message to present at least oneexample of what a caller can say to interact with the NLU application.The system can further include a failure dialog to the developer fordisplaying at least one example that failed to be properly interpreted.

BRIEF DESCRIPTION OF THE DRAWINGS

There are shown in the drawings, embodiments that are presentlypreferred; it being understood, however, that the invention is notlimited to the precise arrangements and instrumentalities shown.

FIG. 1 is a schematic depiction of a system for creating and conveyingan example prompt in accordance with an embodiment of the inventivearrangements disclosed herein;

FIG. 2 is a flowchart for creating and conveying an example prompt inaccordance with an embodiment of the inventive arrangements disclosedherein; and

FIG. 3 is a flowchart illustrating a method for creating and conveyingan example prompt in accordance with an embodiment of the inventivearrangements disclosed herein.

DETAILED DESCRIPTION OF THE INVENTION

FIG. 1 is a schematic diagram illustrating an NLU System 100 forcreating and conveying an example within an NLU application developmentenvironment 127 in accordance with an embodiment of the inventivearrangements disclosed. The NLU System itself would itself work inconjunction with but not limited to a speech recognition system andInteractive Voice Response (IVR) platform (not shown in figure) forspeech based interaction. Alternately, it could also be deployed but notlimited to function in a text mode. The system 100 can include anexternal source 102, an example planner 110, a validation unit 120, anda help message 147. The help message 147 can be presented within thecontext of an NLU application dialogue 149. In one arrangement a failuredialogue 169 can be presented to the developer 101. The failure dialogue169 can be presented for displaying at least one example that failed tobe properly interpreted, and also presenting those example sentencesthat failed in a ranked order. The failure dialogue can help thedeveloper recognize what examples the application is capable ofcorrectly interpreting and help the developer introduce examples thatachieve unambiguous and correct interpretation. The operative aspects ofthe embodiments of the invention are further described herein primarilyin the context of performing call routing. The invention also hasutility in many other contexts, such as handling financial and/orcommercial transactions providing public services and other suchvoice-interactive functions.

A developer 101 can enter an example sentence from the external source102 into the NLU application development environment 127 using theexample planner 110. The external source 102 can be a text prompt fortyping in example sentences, or it can be a database of availablesentences. The example planner 110 can be a software interface orprogram component within the NLU application development environment127. For example, the developer 101 can create or identify an example,such as a sentence or phrase using the external source 102, forassociation with a routing destination supported by the NLU application149.

The developer 101 can select the example and enter the example into theexample planner 110 under the corresponding routing destination. The NLUapplication development environment 127 can accordingly form anassociation between the example and the routing destination forconfiguring the validation unit 120 to correctly interpret the examples.The NLU application development environment 127 can present the helpmessage 147 in the NLU application 149 for conveying the examples thedeveloper submitted through the example planner 110 for presentationthrough the NLU application 149. The validation unit 120 can ensure thatan NLU model within the NLU application can properly interpret theexample, that is, associating the example with the (specified orexpected) routing destination and thereby producing a correctlyinterpreted example. The help message 147 can present the example to acaller of the NLU application for conveying to the caller an examplephrase or sentence that the caller can speak to the NLU application 149.The purpose of the example is to provide a subset of sentences that arerepresentative of caller statements which cause the NLU application 149to interpret them correctly and to route the caller to an appropriaterouting destination. For example, the caller can receive a list ofspoken examples from the help message 147 for making a selection.Alternatively, the caller can submit a spoken request not providedwithin the help message 147 which the NLU application can still properlyinterpret for routing the call to the correct destination. Thevalidation unit 120 ensures the examples presented in the help message147 are correctly interpreted by the NLU models for connecting the userto the correct routing destination.

In one arrangement, the example planner 110 can prompt a developer 101to enter an example under a supported routing destination. The NLUapplication can already support various routing destinations. Thedeveloper 101 does not need to supply a routing destination and canenter the example under an already supported routing destination. Thedeveloper 101 can enter the example into the example planner 110 viatext or voice. The validation unit 120 can process the example with theassociated routing destination for forming an assignment between theexample and the routing destination. During the creation of the NLUmodels the developer 101 can provide an example for an already supportedrouting destination. For example, the developer can enter the example “Iwant to buy a car” under the routing destination “sales”. As anotherexample, the developer can enter the example “I need credit” under therouting destination “finances”. The developer enters examples that aregenerally related to the particular services that are within the scopeof the NLU application being developed, typically choosing examplesrelated to the more frequently requested services.

The examples can be presented to a user of the NLU application forconveying examples of what the user can say to the NLU application. Ahelp message 147 can present examples for the user to hear. The user canrepeat an example back or they can submit their own utterance during theinteraction. The examples can be inserted into the help message 147within an NLU application dialogue menu and presented to the callerduring an NLU session. For instance, the help message can present one ormore examples in a sentence frame like, “For example, you could say, ‘Iwant to buy a car’, ‘I need an oil change’, or ‘I have a flat tire’.”The NLU application can interpret the caller request and route thecaller to the correct routing destination.

In one arrangement, the example planner 110 can be a graphical userinterface or information window that can interface with the developer101. The example planner 110 can provide an interface to the developerto enter examples from the external source 102. The example planner 110can prompt a developer 101 to enter an example. The developer 101 canenter the example from the external source 102 by any suitable methodwhich can include, without limitation, typing in the example or copyingthe example from a database. The example planner 110 can add the exampleand associated routing destination as a marked “example” sentence to theNLU database 130. The marked sentence can indicate a considerationpriority during NLU model training, and can be marked with a count. Thecount denotes the frequency of occurrence for entering the example intothe training unit 122. For example, certain examples can be given ahigher count to emphasize a more frequent use and to give more weightingfor purposes of strengthening an interpretation. The NLU database 130can include a large number of marked examples that can be input to theNLU models within the NLU application 120. The validation unit 120 cantake the marked examples provided by the developer 101 and ensure thatthe NLU models can properly interpret each example sentence. Forexample, within a call routing application, the proper interpretation isassignment to the correct routing destination. If the validation unit120 cannot correctly interpret the routing destination from the example,the example sentence can be added to the NLU models and biased to ensurea high likelihood of correct interpretation after retraining the model.

The example planner 110 can also be cooperatively connected to thevalidation unit 120 for identifying the marked sentence within the NLUdatabase 130. The validation unit 120 can perform the training of theNLU models, the testing of the NLU models, and the biasing of the NLUmodels. In one arrangement, the validation unit 120 can include atraining unit 122 cooperatively connected to a testing unit 124 that iscooperatively connected to a biasing unit 126. The units 122, 124, and126 can also be interconnected together. The training unit 122 can becoupled to the NLU sentence database 130 for updating a NLU model wherethe NLU sentence database 130 can include the example and routingdestination. The testing unit 124 can evaluate the performance of NLUmodels with the marked sentences. The biasing unit 126 can tune the NLUmodels during training to associate routing destinations for correctlyinterpreting the examples. The NLU models can be but not limited tostatistical models or grammars or language models. During the trainingof the NLU models, the validation unit 120 can automatically search forsentences marked as examples. The validation unit 120 can extract thesentence text and the routing destination from NLU database 130 andbuild NLU models with processing logic from the NLU applicationdevelopment environment 127.

The testing unit 124 can test the example sentences, compare the outputof the NLU models from the example sentences, and determine if theoutput is the correct routing destination. If the output of the NLUmodel is not the expected routing destination, the training unit canautomatically correct errors using available techniques, including butnot limited to biasing the NLU models to the example sentences thatfailed by adding them to the training data, and restarting the trainingprocess. The validation unit 120 can further include an error resolutionprocedure that can repeat, in a looping fashion, until either allproblems are resolved or a stop condition is reached. A stop conditioncan be, for example, a maximum number of iterations or a threshold ratioof corrected sentences to newly broken sentences.

Referring to FIG. 2, a flowchart for creating an example prompt isshown. When describing the method 200, reference will be made to FIG. 1,although the method can be practiced in any other suitable device orsystem. At step 202, the system 100 can prompt the developer 101 for anexample for a routing destination. The developer 101 can enter anexample from the external source 102, which can be a sentence corpus.The example can be a sentence or phrase that the developer 101associates with the routing destination. For example, a developercreates an NLU application to route calls received by an autodealership. The calls can be routed to departments such as “sales”,“service”, “roadside assistance”, or “finance”, which are the routingdestinations in this example. The developer provides an example of asentence for each of the routing destinations by entering the examplesinto the example planner 110. For example, the developer can enter theexample sentences “I want to buy a car” (associated with sales), “I needan oil change” (associated with service), and “I have a flat tire”(associated with roadside assistance). The developer may provide a fewexamples for the most frequently called destinations.

At step 204, the examples can be added to the NLU database 130. Thevalidation unit 120 can verify the association of the the examples withtheir routing destinations. The examples provided by the developer withthe associated routing destinations can be added as marked examplesentences to an NLU sentence database 130 which may already containgeneral corpora of sentences. The validation unit 120 can take theexamples provided by the developer and ensure that the NLU models canproperly interpret each example sentence. The example planner 110submits examples that fail testing within the validation unit 120. Forexample, within a call routing application, the proper interpretation isassignment to the correct routing destination. If the example fails tomake the correct assignment, the example is added to the NLU databasefor learning an association for making a correct interpretation. The NLUapplication is sufficiently capable of interpreting the example if itpasses the validation unit 120 test.

At step 206, the system 100 can build NLU models. For example, referringto FIG. 1, the training unit 122 builds a NLU model from all sentencesin the NLU database including examples that failed, where the process ofbuilding captures the statistics of the marked example sentences withinthe NLU sentence database. The NLU model forms new rules during thebuilding process to learn an association between the sentences in theNLU database and the routing destination. This enables the NLU model tolearn to interpret the failed examples correctly. The statisticsassociated with the identified NLU model interpret the caller's requestand connect the caller to the correct routing destination.

At step 208, the system 100 can validate NLU models as mentioned. Forexample, referring to FIG. 1, the developer 101 enters an examplesentence into the example planner 110 which the validation unit 120evaluates. If the NLU models within the validation unit do not produce acorrect routing destination with respect to the example sentence, theexample planner 110 adds the example sentence to the NLU database 130.The developer can optionally specify a count to increase the number ofpresentations of the example during training. The developer increasesthe frequency of example occurrence to reduce ambiguities between afrequently used example and its associated routing destination. If theNLU models do produce a correct routing destination with respect to theexample sentence, the example planner 110 does not add the example tothe NLU database 130. Notably, the validation unit 120 trains a set ofNLU models to form associations between the examples and associatedrouting destinations for example sentences that fail. The training unit122 teaches the NLU models to capture statistical information fromsentence content of the marked examples, and biases the statistics tofavor associations concerning the example or multiple occurrences of theexample provided by the developer.

At step 210, a criterion can be measured to reveal the degree to which aNLU model has learned an association between an example and a routingdestination for correctly interpreting the example. For example,referring to FIG. 1, the testing unit 124 measures a discrepancy betweenan example and routing destination during testing. The example andexpected outcome constitute an example sentence which the exampleplanner 110 adds to the NLU database 130. The testing unit 124 firsttests the example to determine if the NLU models identifies a correctrouting destination. If the NLU models can identify a correct routingdestination the example planner 110 does not add the example to the NLUdatabase 130, as there is no need to make any corrections for examplesthat are working properly. If the validation unit 120 cannot identify acorrect routing destination, the example planner 110 adds the failingexamples to the NLU database and trains the NLU models. The trainingunit 122 trains the NLU models to ensure the NLU models can properlyinterpret the example sentence.

During training, the validation unit 120 gathers statistics from thesentences and words in the examples to form associations within the NLUdatabase 130. The NLU models produce a measure of similarity between theexamples and associated routing destinations with regard to statisticswithin the NLU application 149. The measure of similarity provides acriterion for which correct routing destinations are measured. Forexample, the training unit 122 applies weights to the grammar rules tobias the NLU models in favor of frequently called destinations or theexample sentences provided by the developer 101 during generation of theNLU application. The testing unit 124 evaluates the interpretation ofthe NLU models to the trained example sentences, and if the criterion isnot met, the training unit 122 continues to bias the NLU models untilthe criterion is satisfied.

At step 212, the NLU models can be corrected if the criterion is notmet. For example, referring to FIG. 1, the testing unit 124 evaluates adiscrepancy for a developer example. A discrepancy can exist when thetesting unit 124 incorrectly interprets the example sentence. Thetesting unit 124 marks the example within the NLU database 130 forfurther biasing. The biasing unit 126 evaluates the discrepancy andsends the discrepancy with the example and incorrect routing destinationto the training unit 122 for further training. The training unit 122uses the discrepancy with the marked example sentence to further biasthe NLU models to strengthen an association between the example and thecorrect routing destination.

At step 214, if the criterion is met, the system can stop training theNLU models. For example, a developer 101 can enter an example thatalready has an associated routing destination. The developer 101 canenter numerous routing examples each with their own set of routingdestinations. The example planner 110 enters this information into theNLU database 130 if the examples are not properly interpreted duringtesting. The validation unit 120 continues to train the examples andtest the examples to ensure a high likelihood of correct interpretation.The biasing unit 126 controls the amount of biasing to prevent the NLUmodels from incorrectly interpreting sentences that were alreadycorrectly interpreted before the biasing. For example, the training unit122 can over-train the NLU models, which can lessen the interpretationcapabilities of the models. With too much training, the NLU models canbe over-biased, reducing the generalizations of the discriminantfunctions within the NLU models and forcing them to reduce theirvariance—an undesirable outcome. Accordingly, the example planner 110adds examples to the NLU database 130 to increase the statisticalvariance, thereby providing focused generalizations that are modecontent specific. When the validation unit 120 determines the NLU modelsthat can accurately interpret the example, and provide the correctinterpretation, the training unit 122 stops training.

Referring to FIG. 3, a method 300 for creating an example prompt isshown. When describing the method 300, reference will be made to FIG. 1,although the method can be practiced in any other suitable device orsystem. Moreover, the steps of the method 300 are not limited to theparticular order in which they are presented in FIG. 3. The inventivemethod can also have a greater number of steps or a fewer number ofsteps than those shown in FIG. 3.

At step 302, a context of the NLU application within which an examplecan be presented can be identified. For this example, an auto dealershipNLU system may receive calls that can be routed to various departments.

At step 304, the system 100 can prompt the developer for an example.Referring to FIG. 1, the example planner 110 presents a prompt to thedeveloper for entering an example. For example, in the case of the autodealership NLU routing application, the developer can enter the examples“I want to buy a car” (associated with sales), “I need an oil change”(associated with service), and “I have a flat tire (associated withroadside assistance). Also, the NLU application development environmentor NLU software programming environment 127 may contain drag and droprouting destinations which a developer may use to enter the examples.The developer 101 can build the NLU application using this NLUapplication development environment. The developer 101 can write an NLUprogram on software in a software programming environment. The softwareprogramming environment can include application interfaces toinformation sources. For example, the software programming environmentcould link in example sentences from an external source 102. Thedeveloper can enter program commands to copy the example sentences, orthe developer can use drag and drop features for entering the examplesinto the application.

The developer example can be added to a NLU sentence database if theexample fails to interpret the correct routing destination. For example,referring to FIG. 1, the testing unit 124 evaluates an example providedby the developer 101. The testing unit 124 first determines if the NLUmodels can correctly interpret the example, where the test ofinterpretation is whether the NLU models can associate the example withthe correct routing destination. If the NLU models fail to interpret theexample, the example planner 110 adds the example to the NLU database130. If the NLU models correctly interpret the example, and the correctrouting destination is selected for the example, the example planner 110does not add the example to the NLU database.

For example, the developer can enter the example sentence “I need an oilchange” that is associated with a ‘service’ routing destination into theexample planner 110. The testing unit 122 then processes the sentence “Ineed an oil change” to evaluate what routing destination the NLU modelsassociate with the sentence. If the NLU models produce a routingdestination result such as ‘carwash’, the testing unit 124 determinesthat the NLU models did not correctly interpret the sentence.Accordingly, the example planner 110 adds the example sentence to theNLU database with an association to the correct routing destination. TheNLU models, during training, require that a target routing destinationbe associated with the example sentence. Having incorrectly interpretedthe example, the NLU models need to know what should be the correctassociation. For example, the NLU models can be but not limited toHidden Markov Models or Neural Networks that learn associations byminimizing a mean square error or other criterion. The mean square erroror other criterion reveals the degree of association between the exampleand the expected outcome (e.g., the routing destination) which isprovided by the validation unit 120.

Referring to FIG. 1, the example planner 110 marks the example androuting destination within the NLU sentence database to produce a markedexample sentence. The marking identifies the example as one that shouldbe validated and potentially be used for training to improve theaccuracy of the NLU model on these examples. In particular, using thesemarked examples in the training improves the statistical modelingaccuracy by effectively increasing their statistical weighting duringtraining. Accordingly, this biasing increases the association of theexample with the correct routing destination.

At step 310, a correct outcome of the example can be validated. Oneaspect of the present invention includes building the NLU models tovalidate a correct routing destination. Referring to FIG. 1, thetraining unit 122 trains the NLU models using the NLU sentence database,which includes the marked example sentences. The testing unit 124 teststhe NLU models with the marked sentences to produce a result and thebiasing unit 126 biases the NLU models to reinforce the association ofthe example with the correct routing destination based on the result. Inparticular, the validation unit 120 ensures that the NLU applicationlearns an association between the example and associated routingdestination created by the developer during the development of the NLUapplication. In one arrangement, validating a correct outcome caninclude testing the NLU application with the example during development.

At step 312, the NLU models which include the marked example sentencescan be trained. For example, the training unit 122 trains the NLU modelsusing the NLU sentence database with the marked example sentences. Atstep 314, the NLU models can be tested to ascertain whether the modelproduces a correct outcome. In one arrangement, the testing unit 124assesses performance of the NLU models using the example as an input.The biasing unit 126 updates the NLU model to reinforce the associationbetween the example and the correct outcome (i.e., routing destination).For example, the biasing unit 126 measures a degree of correlationbetween the example and the provided routing destination, and biases theNLU model. For instance, the developer can enter many examples into thedeveloper example prompt, each with an associated set of routingdestinations during development. During testing, the NLU models ranks alist of routing destinations based on the degree of correlation from thetraining process. The NLU application selects the routing destinationwith the highest correlation to the example. The correlation valuerepresents the level of confidence within the NLU models for producingthe correct routing destination.

At step 318, a discrepancy can be resolved between the result and thecorrect outcome, and the NLU models can be re-trained. The biasing unit126 resolves this discrepancy by requesting the training unit 122 tore-train the NLU model by including the marked example sentences. Thetraining unit 122 uses these marked example sentences to reinforce theassociation between the marked example sentences and the expectedrouting destination.

In one arrangement, the biasing unit 126 concludes operation by atriggering of a stop condition which prevents further biasing of thetrained NLU model. For example, the triggering can be initiated by adeveloper or a software process during generation of the NLUapplication. The stop condition can also be a threshold of correctedsentences to newly broken sentences. For example, referring to FIG. 1,the biasing unit 126 corrects broken sentences generated by the testingunit 124. The biasing unit 126 passes the corrected sentences to thetraining unit 122, which strengthens the association of the examplesentence to the correct routing destination. In another arrangement adisplay unit 112 shows a failure dialogue 169 coupled to the validationunit 120 and presents an error entry. The failure dialogue 169identifies the response with the correct outcome placed in a list oferror entries to be resolved. The NLU application developmentenvironment 127 presents the failure dialogue 169 to the developer 101.

At step 320, the step of validating the correct outcome (such as routingdestination) can be repeated until all discrepancies are mitigated.Referring to FIG. 1, the testing unit 124 identifies a discrepancyduring the development of the NLU application. The training unit 122includes corrected sentences in the training to reinforce theassociation between the example and the routing destination. The biasingunit 126 sets a threshold and initiates the stop condition when thethreshold of corrected sentences to newly broken sentences is reached.As another example, the biasing unit 126 triggers a stop condition witha threshold number of maximum iterations, where the validation unit 120performs at least one iteration which is considered the validating of atleast one correct outcome.

At step 322 a failure dialogue presents an error entry identifying theresponse and the correct outcome placed in a list of error entries to beresolved can be displayed. For example, referring to FIG. 1, the NLUapplication development environment 127 presents a failure dialogue 169to disclose the outcome of the interpretation of the developer'sexamples. The failure dialogue 169 provides feedback to the developer bypresenting a list of entry errors where the NLU models could notproperly interpret the developer's example or set of examples. Forexample, a developer may enter in a sentence which did not produce acorrect routing destination. The NLU models may not be able to predictthe correct routing destination even with sufficient training.Accordingly, the failure dialogue 169 presents the results of thevalidation to the developer 101, to inform the developer of the NLUmodel's performance.

The developer 101 may elect to use another example instead of theexample that received a poor performance rating in view of the failuredialogue 169. The failure dialogue 169 serves to assist the developer101 in entering examples which the NLU models can correctly interpretfor providing an unambiguous and correct interpretation. The failuredialogue 169 can present errors in a ranked order of probability. Forexample, the validation unit 120 processes a batch of examples sentenceswith each sentence having a confidence score to each of the availablerouting destinations. For each sentence within a batch, the NLU modelproduces a confidence score describing a probability of the examplebeing associated to the routing destination.

The failure dialogue 169 presents a confidence score for each routingdestination in a ranked order. For example, the developer can enter sixsentences all under a common routing destination of N total destinationspossible. The failure dialogue 169 returns an N-best list for each ofthe six examples revealing the confidence scores, and which should listthe correct routing destination at the top of the list if the examplesare accurately interpreted. The failure dialogue 169 retrains exampleswhich do not produce the correct association, and if it cannot producethe correct association, the confidence scores in the list of errorsshow the developer that the system considers the example one that theNLU model cannot interpret correctly. Accordingly, the developer 101examines the list of errors to determine which examples resulted in anincorrect routing destination. The developer 101 removes these incorrectentries and/or substitutes in new entries to ensure that all examplesprovided to a caller within a help message 147 are interpretedcorrectly.

The present invention may be realized in hardware, software, or acombination of hardware and software. The present invention may berealized in a centralized fashion in one computer system or in adistributed fashion where different elements are spread across severalinterconnected computer systems. Any kind of computer system or otherapparatus adapted for carrying out the methods described herein issuited. A typical combination of hardware and software may be a generalpurpose computer system with a computer program that, when being loadedand executed, controls the computer system such that it carries out themethods described herein.

The present invention also may be embedded in a computer programproduct, which comprises all the features enabling the implementation ofthe methods described herein, and which when loaded in a computer systemis able to carry out these methods. Computer program in the presentcontext means any expression, in any language, code or notation, of aset of instructions intended to cause a system having an informationprocessing capability to perform a particular function either directlyor after either or both of the following: a) conversion to anotherlanguage, code or notation; b) reproduction in a different materialform.

This invention may be embodied in other forms without departing from thespirit or essential attributes thereof. Accordingly, reference should bemade to the following claims, rather than to the foregoingspecification, as indicating the scope of the invention.

1-21. (canceled)
 22. A system for facilitating development, in a naturallanguage understanding (NLU) application development environment, of anNLU model associated with an NLU application, the system comprising: atleast one processor; a database storing information used for trainingone or more NLU models; at least one non-transitory computer-readablestorage medium encoded with instructions that, when executed by the atleast one processor, cause the at least one processor to perform:obtaining at least one expected user entry and a corresponding desiredrouting destination; applying the NLU model to the at least one expecteduser entry to determine whether the NLU model associates the at leastone expected user entry with the desired routing destination; when it isdetermined that the NLU model associates the at least one expected userentry with the desired routing destination, selecting the at least oneexpected user entry for presentation to a user in a help message of theNLU application as an example of input the user could provide to berouted to the desired routing destination; and when it is determinedthat the NLU model does not associate the at least one expected userentry with the desired routing destination, training the NLU model usingtraining data accessed in the database to associate the at least oneexpected user entry with the desired routing destination.
 23. The systemof claim 22, wherein the applying comprises: interpreting, via the NLUmodel, the at least one expected user entry to determine an actualrouting destination for the at least one expected user entry; anddetermining whether the NLU model associates the at least one expecteduser entry with the desired routing destination by comparing the actualrouting destination to the desired routing destination.
 24. The systemof claim 23, wherein, when it is determined that the NLU model does notassociate the at least one expected user entry with the desired routingdestination, the instructions further cause the at least one processorto perform: presenting a failure dialog to a developer of the NLUapplication indicating that the actual routing destination does notmatch the desired routing destination.
 25. The system of claim 22,wherein training the NLU model to associate the at least one expecteduser entry with the desired routing destination comprises: training theNLU model in accordance with a specified training frequency and/or astatistical weighting for the at least one expected user entry.
 26. Thesystem of claim 22, wherein when it is determined that the NLU modeldoes not associate the at least one expected user entry with the desiredrouting destination, the instructions further cause the at least oneprocessor to perform: adding the at least one expected user entry to anNLU entry data set associated with the NLU model; and training the NLUmodel using the data in the NLU entry data set.
 27. The system of claim22, wherein the method further comprises: presenting the at least oneexpected user entry in the help message of the NLU application.
 28. Thesystem of claim 27, wherein the help message comprises a preamblefollowed by the at least one expected user entry.
 29. A method forfacilitating, in a natural language understanding (NLU) applicationdevelopment environment, of an NLU model associated with an NLUapplication, the method comprising: using at least one processor and adatabase storing information used for training one or more NLU models toperform: obtaining at least one expected user entry and a correspondingdesired routing destination; applying the NLU model to the at least oneexpected user entry to determine whether the NLU model associates the atleast one expected user entry with the desired routing destination; whenit is determined that the NLU model associates the at least one expecteduser entry with the desired routing destination, selecting the at leastone expected user entry for presentation to a user during in a helpmessage of the NLU application as an example of input the user couldprovide to be routed to the desired routing destination; and when it isdetermined that the NLU model does not associate the at least oneexpected user entry with the desired routing destination, training theNLU model using training data accessed in the database to associate theat least one expected user entry with the desired routing destination.30. The method of claim 29, wherein the applying comprises:interpreting, via the NLU model, the at least one expected user entry todetermine an actual routing destination for the at least one expecteduser entry; and determining whether the NLU model associates the atleast one expected user entry with the desired routing destination bycomparing the actual routing destination to the desired routingdestination.
 31. The method of claim 30, wherein, when it is determinedthat the NLU model does not associate the at least one expected userentry with the desired routing destination, further comprising:presenting a failure dialog to a developer of the NLU applicationindicating that the actual routing destination does not match thedesired routing destination.
 32. The method of claim 29, whereintraining the NLU model to associate the at least one expected user entrywith the desired routing destination comprises: training the NLU modelin accordance with a specified training frequency and/or a statisticalweighting for the at least one expected user entry.
 33. The method ofclaim 29, wherein when it is determined that the NLU model does notassociate the at least one expected user entry with the desired routingdestination, the method further comprises: adding the at least oneexpected user entry to an NLU entry data set associated with the NLUmodel; and training the NLU model using the data in the NLU entry dataset.
 34. The method of claim 29, wherein the method further comprises:presenting the at least one expected user entry in the help message ofthe NLU application.
 35. The method of claim 34, wherein the helpmessage comprises a preamble followed by the at least one expected userentry.
 36. At least one non-transitory computer-readable storage mediumencoded with instructions that, when executed by a computer system,cause the computer system to perform a method for facilitating, in anatural language understanding (NLU) application developmentenvironment, of an NLU model associated with an NLU application, themethod comprising: obtaining at least one expected user entry and acorresponding desired routing destination; applying the NLU model to theat least one expected user entry to determine whether the NLU modelassociates the at least one expected user entry with the desired routingdestination; when it is determined that the NLU model associates the atleast one expected user entry with the desired routing destination,selecting the at least one expected user entry for presentation to auser during in a help message of the NLU application as an example ofinput the user could provide to be routed to the desired routingdestination; and when it is determined that the NLU model does notassociate the at least one expected user entry with the desired routingdestination, training the NLU model using training data accessed in anNLU database to associate the at least one expected user entry with thedesired routing destination.
 37. The at least one non-transitorycomputer-readable storage medium of claim 36, wherein the applyingcomprises: interpreting, via the NLU model, the at least one expecteduser entry to determine an actual routing destination for the at leastone expected user entry; and determining whether the NLU modelassociates the at least one expected user entry with the desired routingdestination by comparing the actual routing destination to the desiredrouting destination.
 38. The at least one non-transitorycomputer-readable storage medium of claim 37, wherein, when it isdetermined that the NLU model does not associate the at least oneexpected user entry with the desired routing destination, the methodfurther comprises: presenting a failure dialog to a developer of the NLUapplication indicating that the actual routing destination does notmatch the desired routing destination.
 39. The at least onenon-transitory computer-readable storage medium of claim 36, whereintraining the NLU model to associate the at least one expected user entrywith the desired routing destination comprises: training the NLU modelin accordance with a specified training frequency and/or a statisticalweighting for the at least one expected user entry.
 40. The at least onenon-transitory computer-readable storage medium of claim 36, whereinwhen it is determined that the NLU model does not associate the at leastone expected user entry with the desired routing destination, the methodfurther comprises: adding the at least one expected user entry to an NLUentry data set associated with the NLU model; and training the NLU modelusing the data in the NLU entry data set.
 41. The at least onenon-transitory computer-readable storage medium of claim 36, wherein themethod further comprises: presenting the at least one expected userentry in the help message of the NLU application.